Data Source

Source: FEMA, National Risk Index, October 2020 release.

The Data Used here

The National Risk Index is intended to provide a view of the natural hazard risk within communities. While FEMA includes information on 18 natural hazards, we focus on six – coastal flooding, drought, heat wave, hurricane, riverine flooding, and strong wind – pulling measures on

  • frequency (measuring the number of events or event days during a reporting period and the estimated annualized frequency or probability),
  • exposure (measuring the building value, people, or agricultural value exposed to the natural hazard event), and
  • historic loss ratio (measuring the proportion of building value, people, or agricultural value that has been historically impacted by the natural hazard).

The NRI uses data on natural hazards from multiple sources and estimates natural hazard frequency, exposure, and historic loss at the census tract level.

To learn more, see:

Variable descriptions

glimpse(nri)
## Rows: 13
## Columns: 76
## $ OID_       <dbl> 47837, 47838, 63468, 63661, 65308, 65326, 69691, 69695, 697…
## $ NRI_ID     <chr> "T51001090600", "T51001980100", "T51131930100", "T511319302…
## $ STATE      <chr> "Virginia", "Virginia", "Virginia", "Virginia", "Virginia",…
## $ STATEABBRV <chr> "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA", "VA",…
## $ STATEFIPS  <dbl> 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51
## $ COUNTY     <chr> "Accomack", "Accomack", "Northampton", "Northampton", "Nort…
## $ COUNTYTYPE <chr> "County", "County", "County", "County", "County", "County",…
## $ COUNTYFIPS <chr> "001", "001", "131", "131", "131", "001", "001", "001", "00…
## $ STCOFIPS   <dbl> 51001, 51001, 51131, 51131, 51131, 51001, 51001, 51001, 510…
## $ TRACT      <chr> "090600", "980100", "930100", "930200", "930300", "090300",…
## $ TRACTFIPS  <dbl> 51001090600, 51001980100, 51131930100, 51131930200, 5113193…
## $ POPULATION <dbl> 4401, 0, 4376, 3820, 4193, 2335, 2941, 5, 6234, 4907, 2849,…
## $ BUILDVALUE <dbl> 665181000, 3772000, 595521000, 380188000, 603745000, 211228…
## $ AGRIVALUE  <dbl> 14720233.70, 218489.79, 21407021.39, 43535471.38, 31048507.…
## $ AREA       <dbl> 49.325259, 12.157470, 53.135749, 71.502115, 87.054823, 49.5…
## $ CFLD_EVNTS <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
## $ CFLD_AFREQ <dbl> 2.743370, 2.709987, 1.202226, 1.461385, 1.292462, 2.482070,…
## $ CFLD_EXPB  <dbl> 367905008, 3772000, 110856599, 86004724, 310758713, 1348494…
## $ CFLD_EXPP  <dbl> 2434.14941, 0.00000, 814.59508, 864.14628, 2158.21462, 1490…
## $ CFLD_EXPPE <dbl> 18012705627, 0, 6028003599, 6394682493, 15970788179, 110310…
## $ CFLD_EXPT  <dbl> 18380610635, 3772000, 6138860198, 6480687217, 16281546892, …
## $ CFLD_HLRB  <dbl> 0.001493404, 0.001493404, 0.003208678, 0.003208678, 0.00320…
## $ CFLD_HLRP  <dbl> 3.011133e-07, 3.011133e-07, 3.011133e-07, 3.011133e-07, 3.0…
## $ CFLD_HLRR  <chr> "Very Low", "Relatively High", "Relatively Low", "Relativel…
## $ DRGT_EVNTS <dbl> 91, 42, 28, 28, 28, 63, 42, 42, 42, 35, 42, 28, 42
## $ DRGT_AFREQ <dbl> 5.055556, 2.333333, 1.555556, 1.555556, 1.555556, 3.500000,…
## $ DRGT_EXPB  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
## $ DRGT_EXPP  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
## $ DRGT_EXPPE <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
## $ DRGT_EXPA  <dbl> 4848309, 0, 0, 43535471, 23747093, 0, 0, 0, 39002636, 30104…
## $ DRGT_EXPT  <dbl> 4848309, 0, 0, 43535471, 23747093, 0, 0, 0, 39002636, 30104…
## $ DRGT_HLRB  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
## $ DRGT_HLRP  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
## $ DRGT_HLRA  <dbl> 0.001584889, 0.001584889, 0.003693487, 0.003693487, 0.00369…
## $ DRGT_HLRR  <chr> "Relatively Moderate", "No Rating", "No Rating", "Very High…
## $ HWAV_EVNTS <dbl> 6, 11, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, 11
## $ HWAV_AFREQ <dbl> 0.4942339, 0.5766009, 0.5766063, 0.5766063, 0.5766063, 0.49…
## $ HWAV_EXPB  <dbl> 665180718, 3772000, 595520944, 380187676, 603744461, 211227…
## $ HWAV_EXPP  <dbl> 4400.999, 0.000, 4375.999, 3819.996, 4192.997, 2334.998, 29…
## $ HWAV_EXPPE <dbl> 32567391524, 0, 32382392576, 28267973910, 31028174325, 1727…
## $ HWAV_EXPT  <dbl> 33232572241, 3772000, 32977913520, 28648161585, 31631918786…
## $ HWAV_HLRB  <dbl> 3.60888e-10, 3.60888e-10, 2.97900e-12, 2.97900e-12, 2.97900…
## $ HWAV_HLRP  <dbl> 2.901945e-07, 2.901945e-07, 2.901945e-07, 2.901945e-07, 2.9…
## $ HWAV_HLRR  <chr> "Very Low", "Very Low", "Very Low", "Very Low", "Very Low",…
## $ HRCN_EVNTS <dbl> 33, 21, 28, 22, 25, 26, 21, 21, 26, 24, 25, 26, 27
## $ HRCN_AFREQ <dbl> 0.1967287, 0.2214110, 0.2239624, 0.2238779, 0.2406999, 0.22…
## $ HRCN_EXPB  <dbl> 664767958, 3769524, 595340857, 380080256, 603484965, 211227…
## $ HRCN_EXPP  <dbl> 4398.720, 0.000, 4374.935, 3818.995, 4191.471, 2334.992, 29…
## $ HRCN_EXPPE <dbl> 32550526424, 0, 32374516646, 28260566446, 31016886195, 1727…
## $ HRCN_EXPT  <dbl> 33215294382, 3769524, 32969857503, 28640646702, 31620371160…
## $ HRCN_HLRB  <dbl> 0.0006539852, 0.0006539852, 0.0011742747, 0.0011742747, 0.0…
## $ HRCN_HLRP  <dbl> 6.528385e-07, 6.528385e-07, 1.377019e-06, 1.377019e-06, 1.3…
## $ HRCN_HLRR  <chr> "Very Low", "Relatively Moderate", "Very Low", "Very Low", …
## $ RFLD_EVNTS <dbl> 13, 13, 6, 6, 6, 13, 13, 13, 13, 13, 13, 13, 13
## $ RFLD_AFREQ <dbl> 0.5909091, 0.5909091, 0.2727273, 0.2727273, 0.2727273, 0.59…
## $ RFLD_EXPB  <dbl> 202317003.4, 3429843.9, 46695447.8, 44570613.2, 149882632.4…
## $ RFLD_EXPP  <dbl> 1408.94892, 0.00000, 276.46151, 329.02285, 763.49921, 919.0…
## $ RFLD_EXPPE <dbl> 10426222026, 0, 2045815190, 2434769122, 5649894147, 6800847…
## $ RFLD_EXPA  <dbl> 3092313.16, 175094.85, 1135609.61, 2408639.57, 1343780.69, …
## $ RFLD_EXPT  <dbl> 1.063163e+10, 3.604939e+06, 2.093646e+09, 2.481748e+09, 5.8…
## $ RFLD_HLRB  <dbl> 0.0004153736, 0.0004153736, 0.0037380237, 0.0037380237, 0.0…
## $ RFLD_HLRP  <dbl> 3.925184e-06, 3.925184e-06, 1.824847e-05, 1.824847e-05, 1.8…
## $ RFLD_HLRA  <dbl> 0.001257540, 0.001257540, 0.008719085, 0.008719085, 0.00871…
## $ RFLD_HLRR  <chr> "Very Low", "Very Low", "Very Low", "Very Low", "Very Low",…
## $ SWND_EVNTS <dbl> 209, 161, 150, 146, 115, 162, 162, 162, 151, 155, 158, 142,…
## $ SWND_AFREQ <dbl> 6.533043, 5.062500, 4.710873, 4.582564, 3.599022, 5.076151,…
## $ SWND_EXPB  <dbl> 665181000, 3772000, 595521000, 380188000, 603745000, 211228…
## $ SWND_EXPP  <dbl> 4401, 0, 4376, 3820, 4193, 2335, 2941, 5, 6234, 4907, 2849,…
## $ SWND_EXPPE <dbl> 32567400000, 0, 32382400000, 28268000000, 31028200000, 1727…
## $ SWND_EXPA  <dbl> 14720233.70, 218489.79, 21407021.39, 43535471.38, 31048507.…
## $ SWND_EXPT  <dbl> 33247301234, 3990490, 32999328021, 28691723471, 31662993507…
## $ SWND_HLRB  <dbl> 1.648158e-05, 1.648158e-05, 1.927672e-05, 1.927672e-05, 1.9…
## $ SWND_HLRP  <dbl> 6.850051e-08, 6.850051e-08, 3.424598e-07, 3.424598e-07, 3.4…
## $ SWND_HLRA  <dbl> 2.435583e-06, 2.435583e-06, 2.435583e-06, 2.435583e-06, 2.4…
## $ SWND_HLRR  <chr> "Very Low", "Very Low", "Very Low", "Very Low", "Very Low",…
## $ NRI_VER    <chr> "October 2020", "October 2020", "October 2020", "October 20…

Observations are census tract estimates of…

  • Population, building value, agricultural value, and area within tract
  • Natural hazards include: CFLD - coastal flooding, DRGT - drought, HWAV - heat wave, HRCN - hurricane, RFLD - riverine flooding, SWND - strong wind
  • Hazard measures include: EVNTS - number of events in recording period, AFREQ - annualized frequency (# events/# years in recording period)
  • Exposure measures include: EXPB - building value exposure, EXPP - population exposure, EXPE - population equivalence exposure, EXPA - agricultural value exposure
  • Historic loss ratio measures include: HLRB - historic loss ratio for building value, HLRA - historicla loss ratio for agriculture, HLRP - historical loss ratio for population, HLRR - historic loss ratio overall

Summaries

5-number summaries of (non-missing) numeric variables (remove tract identifiers)

nri %>% select(-c(OID_:STATEFIPS, COUNTYTYPE:TRACTFIPS, NRI_VER)) %>% 
  select(where(~is.numeric(.x) && !is.na(.x))) %>% 
  as.data.frame() %>% 
  stargazer(., type = "text", title = "Summary Statistics", digits = 0,
            summary.stat = c("mean", "sd", "min", "median", "max"))
## 
## Summary Statistics
## ================================================================================
## Statistic       Mean         St. Dev.       Min        Median          Max      
## --------------------------------------------------------------------------------
## POPULATION     3,504          1,942          0         3,820          6,234     
## BUILDVALUE  445,064,231    263,814,332   3,772,000  547,772,000    813,756,000  
## AGRIVALUE    19,943,077     15,182,275    31,301     21,407,021     43,535,471  
## AREA             51             27           7           53             87      
## CFLD_AFREQ       2              1            1           2              3       
## CFLD_EXPB   199,509,533    198,615,187   3,772,000  134,849,422    736,404,000  
## CFLD_EXPP      1,402           936           0         1,452          2,941     
## CFLD_EXPPE 10,371,633,091 6,922,727,785      0     10,746,133,053 21,763,400,000
## CFLD_EXPT  10,571,142,624 7,099,434,508  3,772,000 10,926,364,382 22,499,804,000
## CFLD_HLRB        0              0            0           0              0       
## CFLD_HLRP        0              0            0           0              0       
## DRGT_EVNTS       43             17          28           42             91      
## DRGT_AFREQ       2              1            2           2              5       
## DRGT_EXPA    12,767,239     16,840,163       0           0          43,535,471  
## DRGT_EXPT    12,767,239     16,840,163       0           0          43,535,471  
## DRGT_HLRA        0              0            0           0              0       
## HWAV_EVNTS       7              2            6           6              11      
## HWAV_AFREQ       1              0            0           0              1       
## HWAV_EXPB   445,064,081    263,814,292   3,772,000  547,771,848    813,755,973  
## HWAV_EXPP      3,504          1,942          0         3,820          6,234     
## HWAV_EXPPE 25,930,157,957 14,370,703,000     0     28,267,973,910 46,131,584,530
## HWAV_EXPT  26,375,222,038 14,594,282,423 3,772,000 28,648,161,585 46,679,356,378
## HWAV_HLRB        0              0            0           0              0       
## HWAV_HLRP        0              0            0           0              0       
## HRCN_EVNTS       25             3           21           25             33      
## HRCN_AFREQ       0              0            0           0              0       
## HRCN_EXPB   444,801,528    263,624,465   3,769,524  547,726,825    813,598,176  
## HRCN_EXPP      3,503          1,942          0         3,819          6,234     
## HRCN_EXPPE 25,920,530,241 14,369,165,198     0     28,260,566,446 46,129,744,254
## HRCN_EXPT  26,365,331,769 14,592,649,165 3,769,524 28,640,646,702 46,677,471,079
## HRCN_HLRB        0              0            0           0              0       
## HRCN_HLRP        0              0            0           0              0       
## RFLD_EVNTS       11             3            6           13             13      
## RFLD_AFREQ       1              0            0           1              1       
## RFLD_EXPB   106,469,319    161,601,174    301,320    51,513,125    608,324,065  
## RFLD_EXPP       605            662           0          340           2,374     
## RFLD_EXPPE 4,479,443,695  4,897,974,424      0     2,517,678,037  17,566,176,693
## RFLD_EXPA    1,935,370      1,612,333     26,074     1,964,586      5,149,665   
## RFLD_EXPT  4,587,848,385  5,052,492,472   364,660  2,574,672,609  18,174,526,832
## RFLD_HLRB        0              0            0           0              0       
## RFLD_HLRP        0              0            0           0              0       
## RFLD_HLRA        0              0            0           0              0       
## SWND_EVNTS      156             20          115         158            209      
## SWND_AFREQ       5              1            4           5              7       
## SWND_EXPB   445,064,231    263,814,332   3,772,000  547,772,000    813,756,000  
## SWND_EXPP      3,504          1,942          0         3,820          6,234     
## SWND_EXPPE 25,930,169,231 14,370,706,471     0     28,268,000,000 46,131,600,000
## SWND_EXPA    19,943,077     15,182,275    31,301     21,407,021     43,535,471  
## SWND_EXPT  26,395,176,538 14,606,328,047 3,990,490 28,691,723,471 46,718,374,636
## SWND_HLRB        0              0            0           0              0       
## SWND_HLRP        0              0            0           0              0       
## SWND_HLRA        0              0            0           0              0       
## --------------------------------------------------------------------------------

Summaries of (non-missing) character variables (remove tract identifiers)

nri %>% select(-c(OID_:STATEFIPS, COUNTYTYPE:TRACTFIPS, NRI_VER)) %>% 
  select(where (~is.character(.x))) %>% map(tabyl)
## $COUNTY
##      .x[[i]]  n   percent
##     Accomack 10 0.7692308
##  Northampton  3 0.2307692
## 
## $CFLD_HLRR
##              .x[[i]] n    percent
##      Relatively High 1 0.07692308
##       Relatively Low 4 0.30769231
##  Relatively Moderate 1 0.07692308
##             Very Low 7 0.53846154
## 
## $DRGT_HLRR
##              .x[[i]] n   percent
##            No Rating 7 0.5384615
##  Relatively Moderate 4 0.3076923
##            Very High 2 0.1538462
## 
## $HWAV_HLRR
##   .x[[i]]  n percent
##  Very Low 13       1
## 
## $HRCN_HLRR
##              .x[[i]]  n    percent
##       Relatively Low  1 0.07692308
##  Relatively Moderate  1 0.07692308
##             Very Low 11 0.84615385
## 
## $RFLD_HLRR
##   .x[[i]]  n percent
##  Very Low 13       1
## 
## $SWND_HLRR
##   .x[[i]]  n percent
##  Very Low 13       1

Visual distribution

Frequency distribution across tracts:

Tract assets

nri %>% select(TRACTFIPS:AREA) %>% 
  pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>% 
  ggplot(aes(x = value, fill = measure)) + 
  scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
  geom_histogram() + 
  facet_wrap(~measure, scales = "free")

Tract hazards: Coastal Flooding

# Tract hazards: CFLD
nri %>% select(contains("CFLD"), TRACTFIPS) %>% select(-contains("HLRR")) %>% 
  pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>% 
  ggplot(aes(x = value, fill = measure)) + 
  geom_histogram() + 
  scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
  facet_wrap(~measure, scales = "free")

Tract hazards: Drought

nri %>% select(contains("DRGT"), TRACTFIPS) %>% select(-contains("HLRR")) %>% 
  pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>% 
  ggplot(aes(x = value, fill = measure)) + 
  geom_histogram() + 
  scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
  facet_wrap(~measure, scales = "free")

Tract hazards: Heat Wave

nri %>% select(contains("HWAV"), TRACTFIPS) %>% select(-contains("HLRR")) %>% 
  pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>% 
  ggplot(aes(x = value, fill = measure)) + 
  geom_histogram() + 
  scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
  facet_wrap(~measure, scales = "free")

Tract hazards: Hurricane

nri %>% select(contains("HRCN"), TRACTFIPS) %>% select(-contains("HLRR")) %>% 
  pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>% 
  ggplot(aes(x = value, fill = measure)) + 
  geom_histogram() + 
  scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
  facet_wrap(~measure, scales = "free")

Tract hazards: Riverine Flooding

nri %>% select(contains("RFLD"), TRACTFIPS) %>% select(-contains("HLRR")) %>% 
  pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>% 
  ggplot(aes(x = value, fill = measure)) + 
  geom_histogram() + 
  scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
  facet_wrap(~measure, scales = "free")

Tract hazards: Strong Wind

nri %>% select(contains("SWND"), TRACTFIPS) %>% select(-contains("HLRR")) %>% 
  pivot_longer(-TRACTFIPS, names_to = "measure", values_to = "value") %>% 
  ggplot(aes(x = value, fill = measure)) + 
  geom_histogram() + 
  scale_fill_viridis(option = "plasma", discrete = TRUE, guide = FALSE) +
  facet_wrap(~measure, scales = "free")

Maps

Variation across tracts

Coastal Flooding

# CFLD
pal <- colorNumeric("plasma", reverse = TRUE, domain = eastern_nri$CFLD_AFREQ) # viridis

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = eastern_nri,
              fillColor = ~pal(CFLD_AFREQ),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("Tract Number: ", eastern_nri$NAME, "<br>",
                             "Ann. Freq.: ", round(eastern_nri$CFLD_AFREQ, 2))
  ) %>% 
  addLegend("bottomright", pal = pal, values = eastern_nri$CFLD_AFREQ, 
            title = "Coastal Flooding-#/year", opacity = 0.7)

Droughts

# DRGT
pal <- colorNumeric("plasma", reverse = TRUE, domain = eastern_nri$DRGT_AFREQ) # viridis

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = eastern_nri,
              fillColor = ~pal(DRGT_AFREQ),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("Tract Number: ", eastern_nri$NAME, "<br>",
                             "Ann. Freq.: ", round(eastern_nri$DRGT_AFREQ, 2))
  ) %>% 
  addLegend("bottomright", pal = pal, values = eastern_nri$DRGT_AFREQ, 
            title = "Drought-#/year", opacity = 0.7)

Heat Wave

# HWAV
pal <- colorNumeric("plasma", reverse = TRUE, domain = eastern_nri$HWAV_AFREQ) # viridis

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = eastern_nri,
              fillColor = ~pal(HWAV_AFREQ),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("Tract Number: ", eastern_nri$NAME, "<br>",
                             "Ann. Freq.: ", round(eastern_nri$HWAV_AFREQ, 2))
  ) %>% 
  addLegend("bottomright", pal = pal, values = eastern_nri$HWAV_AFREQ, 
            title = "Heat Wave-#/year", opacity = 0.7)

Hurricane

# HRCN
pal <- colorNumeric("plasma", reverse = TRUE, domain = eastern_nri$HRCN_AFREQ) # viridis

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = eastern_nri,
              fillColor = ~pal(HRCN_AFREQ),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("Tract Number: ", eastern_nri$NAME, "<br>",
                             "Ann. Freq.: ", round(eastern_nri$HRCN_AFREQ, 2))
  ) %>% 
  addLegend("bottomright", pal = pal, values = eastern_nri$HRCN_AFREQ, 
            title = "Hurricane-#/year", opacity = 0.7)

Riverine Flooding

# RFLD
pal <- colorNumeric("plasma", reverse = TRUE, domain = eastern_nri$RFLD_AFREQ) # viridis

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = eastern_nri,
              fillColor = ~pal(RFLD_AFREQ),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("Tract Number: ", eastern_nri$NAME, "<br>",
                             "Ann. Freq.: ", round(eastern_nri$RFLD_AFREQ, 2))
  ) %>% 
  addLegend("bottomright", pal = pal, values = eastern_nri$RFLD_AFREQ, 
            title = "Riverine Flooding-#/year", opacity = 0.7)

Strong Wind

# SWND
pal <- colorNumeric("plasma", reverse = TRUE, domain = eastern_nri$SWND_AFREQ) # viridis

leaflet() %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addPolygons(data = eastern_nri,
              fillColor = ~pal(SWND_AFREQ),
              weight = 1,
              opacity = 1,
              color = "white", 
              fillOpacity = 0.6,
              highlight = highlightOptions(
                weight = 2,
                fillOpacity = 0.8,
                bringToFront = T
              ),
              popup = paste0("Tract Number: ", eastern_nri$NAME, "<br>",
                             "Ann. Freq.: ", round(eastern_nri$SWND_AFREQ, 2))
  ) %>% 
  addLegend("bottomright", pal = pal, values = eastern_nri$SWND_AFREQ, 
            title = "Strong Wind-#/year", opacity = 0.7)

Nota Bene

  • Several hazard rates are dominated by regional measures, with little variation identified within the region.